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Record W4154482 · doi:10.17705/1jais.00343

The Influence of Analyst Communication in IS Projects

2013· article· en· W4154482 on OpenAlex
Shadi Shuraida, Henri Barki

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the Association for Information Systems · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsTask (project management)Knowledge managementInformation needsComputer scienceInformation systemWorld Wide WebEngineering

Abstract

fetched live from OpenAlex

Information system (IS) researchers have long noted that IS analysts need to understand users’ needs if they are to design better systems and improve project outcomes. While researchers agree that analyst communication activities are an important prerequisite for such an understanding, little is known about the nature of different communication behaviors IS analysts can undertake to learn about users’ system needs and the impact of such behaviors on IS projects. To address this gap, this paper draws from the learning literature to articulate the information transmission activities IS analysts can undertake and the content of the information they can transmit when learning about users’ organizational tasks and information needs. The influence of analyst communication activities on the generation of valid information regarding user needs, analyst learning, and IS project outcomes are then investigated via a case study of two IS projects. The analysis of the two cases suggests that analysts who encourage the use of concrete examples, testing, and validation, and who solicit feedback about users’ business processes are likely to better understand users’ tasks, and in turn design systems that better meet users’ task needs than analysts who do not.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.282
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it